The presence of physiological noise in functional MRI can greatly limit the sensitivity and accuracy of BOLD signal measurements, and produce significant false positives. There are two main types of physiological confounds: (1) high-variance signal in non-neuronal tissues of the brain including vascular tracts, sinuses and ventricles, and (2) physiological noise components which extend into gray matter tissue. These physiological effects may also be partially coupled with stimuli (and thus the BOLD response).
One of the major challenges of BOLD fMRI is to identify and control BOLD physiological signal. These confounds are caused by a variety of mechanisms, including
- the flow of blood and cerebrospinal fluid (CSF) driven by cardiac pulsation (Dagli et al., 1999);
- phase distortion and motion artifact due to respiration (Raj et al., 2001; Windischberger et al., 2002);
- fluctuations in O2/CO2 levels, driven by changes in respiratory and cardiac rates (Wise et al., 2004; Birn et al., 2006; Shmueli et al., 2007);
- less studied phenomena, such as vasomotion (Mayhew et al., 1996) and metabolic-linked (Yang et al., 2009) effects.
The changes in fMRI signal due to physiological effects have complex, subjectdependent spatial and temporal structure, making them difficult to separate from the BOLD response.
(The above is taken from: Churchill, N. W., & Strother, S. C. (2013). PHYCAA+: An Optimized, Adaptive Procedure for Measuring and Controlling Physiological Noise in BOLD fMRI. Neuroimage. http://www.ncbi.nlm.nih.gov/pubmed/23727534.)
One possible approach to this problem - measure respiratory and cardiac data during scanning and use it as a nuisance regressor in GLM. This page will cover two main topics:
- How to do this at the CBU?
- Is it worth doing?